A New Blind Image Quality Assessment Based on Pairwise

Recently, the algorithms of general purpose blind image quality assessment (BIQA) have been an important research area in the field of image processing, but the previous approaches usually depend on human scores image for training and using the regression methods to predict the image quality. In this paper, we first apply the full-reference image quality measure to obtain the image quality scores for training to let our algorithm independent of the judgment of human. Then, we abstract features using an NSS model of the image DCT coefficient which is indicative of perceptual quality, and subsequently, we import Pairwise approach of Learning to rank (machine-learned ranking) to predict the perceptual scores of image quality. Our algorithm is tested on LIVE II and CSIQ database and it is proved to perform highly correlate with human judgment of image quality, and better than the popular SSIM index and competitive with the state-of-the-art BIQA algorithms.